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Big Data Gaat het iets voor de zorg betekenen? Dr N.S. Hekster

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Presentatie over: "Big Data Gaat het iets voor de zorg betekenen? Dr N.S. Hekster"— Transcript van de presentatie:

1 Big Data Gaat het iets voor de zorg betekenen? Dr N.S. Hekster
18 maart 2015

2 Introductie Spreker Nicky Hekster
Technical Leader Healthcare & LifeSciences IBM Nederland BV Johan Huizingalaan 765 1066 VH Amsterdam Mobile:

3 Artsen weten vaak niet welke behandeling de beste is Januari 2015
Medisch specialisten weten in veel gevallen niet wat voor hun patiënt de juiste behandeling is. Artsen kunnen bij aandoeningen vaak kiezen uit verschillende behandelingen. In onderzoek van de Orde van Medisch Specialisten en EenVandaag geeft 61 % van de artsen aan in minstens 25% van de gevallen niet te weten welke behandeling de beste is. Aan het onderzoek deden ruim 2000 artsen mee.

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5 Patiëntregistraties Nictiz, het Zorginstituut en elf zorgpartijen zorgen voor één landelijk overzicht 135 verschillende patiëntenregistraties, 35 zorgspecialismen. 88 verschillende organisaties beheren deze patiëntenregistraties. Daarnaast zijn 32 registraties in de inventarisatie opgenomen die niet direct patiëntgegevens vastleggen, maar wel betrekking hebben op de kwaliteit van de zorg. Patiëntenregistraties leveren een waardevolle bijdrage om de kwaliteit van de zorg te verbeteren. Het landelijke overzicht maakt voor iedereen inzichtelijk welke patiëntenregistraties er beschikbaar zijn en dat komt de transparantie ten goede. Zorgleveranciers kunnen dit overzicht ook bijvoorbeeld voor hun informatiebeleid gebruiken.

6 Nieuwe inzichten, onderzoek en voortuitgang doen de complexiteit van zorg, onderzoek, en onderwijs toenemen Elke dag worden artsen geconfronteerd met … Begrijpen van de toestand van de patiënt Formuleren van behandelopties Selectie van een persoonlijk behandelplan Medische informatie verdubbelt elke 5 jaar. 80% van de zorgprofessionals besteedt ten hoogste 5 uur/maand om bij te blijven. Slechts 20% van de kennis die artsen/behandelaren gebruiken is evidence based: 1 op de 5 diagnosen zijn niet correct of incompleet . …gegeven verschillende databronnen van variërende volledigheid en betrouwbaarheid …gebaseerd op de laatste richtlijnen en medische literatuur …gebaseerd op comorbiditeit, voorwaarden, CIA, bijwerkingen, en de patient’s specifieke wensen of klinische attributen Elke dag worden onderzoekers geconfronteerd met … Bijhouden van de medische literatuur Ontdekken en naar boven halen van nieuwe verbanden Genereren van nieuwe inzichten en vermoedens voor toekomstig onderzoek … steeds toenemend volume aan wetenschappelijke medische artikelen en boeken …het kijken naar verschillende disciplines en het ontdekken van nieuwe relaties tussen ziekten, genetica en medicatie …ontwikkeling van valide hypothesen om tot nieuwe ontdekkingen te komen

7 De patiënt zelf als informatiebron
IBM IOD 2012 4/9/2017 De patiënt zelf als informatiebron The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care [1]. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management [2-5]. Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes) [6]. Kaiser Permanente, the California-based health network, which has more than 9 million members, is believed to have between 26.5 and 44 petabytes of potentially rich data from EHRs, including images and annotations [6]. By definition, big data in healthcare refers to electronic health data sets so large and complex that they are difficult (or impossible) to manage with traditional software and/or hardware; nor can they be easily managed with traditional or common data management tools and methods [7]. Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed [7]. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. It includes clinical data from CPOE and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called tweets) [8], blogs [9], status updates on Facebook and other platforms, and web pages; and less patient-specific information, including emergency care data, news feeds, and articles in medical journals. only 20 percent of the knowledge physicians use to make diagnosis and treatment decisions today is evidence based. The result? One in five diagnoses are incorrect or incomplete and nearly 1.5 million medication errors are made in the U.S. every year Read more: Drury Design Dynamics

8 Toegang tot relevante patiëntinformatie Toegang tot klinische kennis
Hoezo dus personalized medicine? Vereist betere registratie, toegang tot en analyse van relevante patient- c.q. cliëntinformatie, processen en klinische kennis Meer kunst dan wetenschap Meer wetenschap dan kunst Goed Gepersonaliseerd (Gebaseerd op mensen zoals ik) Toegang tot relevante patiëntinformatie Waarde However, current medical practice does not consider to provide for the collection and access to patient “health-related” data that would be required for personalized medicine. This is a natural progression along the path to better, more scientific and data-driven care. The large arrow shows a progression from practicing medicine based on individual knowledge and experience, through intuitive or consensus-based approaches when evidence is sparse, to evidence based on populations and ultimately to the holy grail of personalized health promotion and care delivery with evidence based on patients like me. Currently, too much care today is “trial and error”, meaning that it is based on individual clinician expertise and knowledge, all-to-frequently with limited access to relevant patient information and clinical knowledge. Healthcare is too complex and changing too fast to base care only on what an individual clinician can learn and retain. In 1975 there were about 200 clinical trials published. By 2005, the number had grown to over 30,000. Add to that all the industry knowledge generated outside of clinical trials… In short, we have increasing complexity of intervention options, increasing insights into patient heterogeneity and an expanding scope of potential services for prevention, chronic care, etc. It is no longer possible to practice medicine “with the knowledge in a clinician’s head.” We don’t have complete knowledge today of all diseases – at it is unlikely that we ever will have complete knowledge. If a physician has access to more complete patient information and clinical knowledge, but knowledge of the disease, interaction of multiple diseases, etc. does not exist then the physician must depend upon his / her intuition to diagnose and determine the best treatment approaches, prognosis, etc. Evidence based approaches can represent a huge step forward. The problem with evidence based on populations is called heterogeneity of treatment effects, which describes the variation in treatment results from the same treatment in different patients. For example, some may respond well to a drug, some may respond but poorly, some may have an adverse reaction and some may have no response. Also, what we think are similar diseases based on symptoms may in fact be quite different diseases. For example, we now know that there are over 90 different types of lymphoma and leukemia. Experts today suggest that we have evidence for only about ¼ to 1/3 of what we do. Also we have been remarkably uncurious regarding what works, why it works and for whom it works. The share of US health expenses devoted to determining what works best is about one-tenth of one percent. Personalized healthcare, in the upper right hand corner, uses more complete information (for example, about the patient, disease states or responses to treatments) to help predict, prevent and aid in early detection of diseases. Then it uses the patient’s unique physiology – and patient preferences, where appropriate – to help determine the best preventive or therapeutic approaches. Regarding the axes, note the reference to diagnostic tools. An incorrect or incomplete diagnosis occurs all too frequently – in up to 50% of cases according to some studies. In a recent report, researchers state that the rate of diagnostic error is up to 15% and that the cases physicians see as routine and unchallenging are often the ones that end up being misdiagnosed. May 2, 2008 in Medscape. Also, autopsies suggest that as many as 20% of fatal illnesses are misdiagnosed. Jerome Groopman, MD and author of “How Doctors Think” suggests that patient pose 3 questions to their doctor when he or she suggests a diagnosis: What else could it be? There are over 10,000 diseases and the biggest diagnostic error is premature closure. Computerized diagnostic tools such as Isabel can help. It is now being interfaced to NextGen. Could two things be going on that would explain my symptoms? Is there anything in my history, physical examination, laboratory findings or other tests that seems not to fit with the diagnosis? Regarding the cause of the disease, we need to know the exact cause, not just the symptoms. A lot of diseases with different causes (and requiring different treatments) share similar symptoms. Regarding comparative effectiveness, we need better information about benefits, risks and costs (for cost effectiveness) for different interventions for different conditions (or multiple conditions) for different patient populations and subpopulations. Also, for the vertical axis, the definition of relevant patient information will expand in a more patient-centric, value-based healthcare system. Clinicians will need to know a lot more about a patient for prevention, prediction, early detection, chronic care coordination, patient compliance and behavior modification than is needed for a specific acute intervention.“ Voorspellend en Evidence-based (Gebaseerd op patiënten- en cliëntencohorts) Intuïtief en volgens klinische consensus/richtlijn (Op basis van partiële toegang tot beschikbare patiënt- en cliëntinformatie en klinische kennis) Proefondervindelijk (Gebaseerd op expertise en ervaring) Matig Matig Goed Toegang tot klinische kennis (e.g. Diagnostische hulpmiddelen, kennis van de oorzaken van ziekten, empirisch bewijs of vergelijkende effectiviteit) Bron: IBM Global Business Services and IBM Institute for Business Value

9 Big Medical Data – opweg naar precisiegeneeskunde Voor de professional en patiënt – van gegevens naar inzicht Coaching bij het nemen van complexe beslissingen Holistische kijk op de patient(encohorts) – personalisatie _______________________________ Coaching bij levensstijl –spiegelinformatie Zelfredzaamheid en verantwoordelijkheid

10 Analytics in de praktijk
Optimalisatie We staan hier! Voorspellen BI Rapportage en ad hoc Analyse Wat is de beste keuze? Wat gaat er gebeuren? Wat zal het gevolg zijn? Wat gebeurde er? Wanneer en waar? Hoeveel? Huidige analytics niveau Transactionele rapportage Data-integratie Data warehouse Voorspellende analyses Decision support analytics Basale rapportage Spreadsheets Financieel, administratief Dashboards Klinische data repositories Afdeling data marts Instellingbrede analytics Evidence-based medicine ROMs Clinical outcomes analytics Clinical Decision Support Gepersonaliseerde zorg Dynamische fraudedetectie Patiëntgedrag Beheersing epidemieën 10

11 Watson – de toekomstige doktersassistent?
Complexe diagnosen Betere en beter afgestemde medicatie of behandelplan Voorkomen van medische missers Evidence Based Medicine (EBD) Natuurlijke taal interface – NLP Menselijk redeneren How Can Watson Help Doctors? “In ‘Jeopardy’, built into the game is this notion confidence; that it’s not worth answering unless you’re sure. And in the real world, there are lots of problems like that. You don’t want your doctor to guess. You want him to have confidence in his answer before he decides to give you a treatment,” commented Dr. Katharine Frase, VP, Industry Solutions and Emerging Business at IBM Research.  Healthcare delivery has become increasingly reliant on a sophisticated mix of medical devices, diagnostic and therapeutic equipment, and the multimodal data they provide. The delivery of high quality healthcare is dependent on a wide variety of diagnostic, surgical, therapeutic systems, the environment of care, as well as supporting IT infrastructures. This abundance of data has created challenges for healthcare providers. “For at least thirty years, it’s been humanly impossible for a physician to master all the material they need to practice at the highest level,” said Dr. Herbert Chase, Professor of Clinical Medicine at Columbia University. “Medical literature has doubled in size every seven years” he added. “You are never going to replace a trained doctor or nurse,” said Dr. Joseph Jasinski, Healthcare and Life Sciences at IBM Research. “But certainly a system like Watson could be a Physician’s Assistant. Suppose you are a clinician, a doctor, a nurse trying to diagnose a very complex case. You have some ideas, but in order to confirm your hypothesis, confirm what you think is wrong; you need a lot of information.” he added In addition to assisting with Medical diagnosis, Watson could help lower the occurrence of Medical errors and Hospital acquired infections, a key issue facing the healthcare system. “Twenty percent of medical errors are diagnostic errors. And it’s not that they’ve missed diagnoses, often that they are delayed.” said Dr. Chase. “Watson has the capacity to get the diagnoses up there sooner” A system like Watson could be tied into a hospital’s facilities and biomedical systems and Healthcare management infrastructure to ensure that assets were cleaned, sanitized or sterilized before they are available for use with a new patient. It could also ensure that clinical equipment which has been recalled or is under a service alert, is removed from service before it is used on a patient. These scenarios would help to proactively reduce the opportunity for medical errors and preventable infections acquired in Hospital. “It is the effective and efficient storage, retrieval, analyses, and use of biomedical information to improve health. At the end of the day, the goal is to improve health,” said Dr. Chase. NLP Ambiguïteiten, in context, impliciet, niet precies Zelflerend 200 miljoen pagina’s/3 sec

12 Slimmere gezondheidszorg d.m.v. analytics
Montpellier Analyse van gestructureerde en ongestructureerde data rondom hoofdhalskanker en andere aandoeningen en correlaties van comorbiditeit. Analyse van juiste eerste behandelopties om erosieve vorm van reumatoide polyartritis tegen te gaan. Creëren van een uitgebreide corpus van gegevens over epilepsie, waaruit via predictive analytics betere behandelingen en medicatievoorschriften worden gedistilleerd. Onderzoek naar de afwijkingen van richtlijnen en best practice behandelingen bij kanker, gegeven kwaliteit en uitkomst. Master Data and Advanced Case Management Data Warehouses, Business Analytics including IBM Netezza based solutions Big data, new data models and more … all based on a common integration framework Same Examples Include: Independent Health Used predictive analytics to create engagement segmentation studies and pinpointed the best way to engage individual customers in lowering health risks through more effective wellness programs. Geisinger Health System Provided rapid analysis and reporting of vital insights from millions of patient encounters through a first-of-its-kind clinical decision intelligence system, improving patient care, research and innovation. North Carolina State University Performed data and content analytics on unstructured information sources to reduce the time needed to find target companies for technology investments from months to days Aetna Used fraud and abuse analytics to identify suspicious physician, hospital, and patient issues in healthcare claims. “To date, the SIU has identified more than $20,000,000 in potential recoveries on the cases.” Not shown: Rice University uses POWER7 for healthcare analytics (http://www-03.ibm.com/press/us/en/pressrelease/29315.wss) Regional University Hospital (supported by Medicaid) Phase 1: Treatment effectiveness through patient monitoring during / post discharge Phase 2: Expand effectiveness and into research areas Solution consists of analyzed patient care information with customized alerting to Medicaid (IBM Content Analytics with medical annotators) Large Healthcare Payer Phase 1: Analyzed patient records for claims processing to reduce cost of evidence collection and automate claims related decisions – expects to save $7.5 million annually Solution to consist of analyzed patient care information with claims (case) data (IBM Content Analytics, IBM Case Manager with medical annotators) Verbetering van kennis over en plannen van heropnames rondom Congestief Hartfalen (CHF) via het correleren van ongestructureerde en gestructureerde data Up-to-date management informatie om competitieve voorsprong te krijgen. Verbetering van de doelmatigheid en kwaliteit van planning en control cyclus.

13 Dank voor uw aandacht!


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